Ontologies are seen as the solution to data
heterogeneity on the web.
However, the available ontologies could themselves
introduce heterogeneity: given two ontologies, the same
entity can be given different names or simply be defined
in different ways, whereas both ontologies may express
the same knowledge but in different languages.

Semantic interoperability can be grounded in ontology
reconciliation.
The underlying problem, which we call the "ontology
alignment'' problem,
can be described as follows: given two ontologies each
describing a set of discrete entities (which can be
classes, properties, predicates, etc.), find the
relationships (e.g., equivalence or subsumption) that
hold between these entities.

Alignment results can further support visualization of
correspondences, transformation of one source into
another or formulation of bridge axioms between the
ontologies.

The alignment problem can be approached from various
standpoints and this fact is reflected in the variety of
alignment methods that have been proposed in the
literature. Many of them are rooted in the classical
problem of schema matching in the database area [4,7]
while others have been specifically designed to work
with ontologies [6,1].
Some methods rely on formal reasoning about the
structure of the entity descriptions [3],
others use a combination of similarity-based and
graph-based reasoning [5]
while a third, mainstream, group apply data analysis [8,9]
or machine learning techniques [2]
to make emerge good alignments. Our own system, OLA [12],
relies on the classical similarity-based paradigm for
entity comparison. The exact similarity measure used by
the system [11]
was derived from the one proposed in [10].